DPM Solver

DPM-solvers are efficient algorithms for sampling from diffusion probabilistic models (DPMs), a class of powerful generative models, aiming to significantly reduce the computational cost of generating high-quality samples. Current research focuses on improving the speed and stability of these solvers, particularly for guided sampling (where the generation process is influenced by external factors), through techniques like improved integration approximation and novel high-order solvers such as DPM-Solver++. These advancements are impactful because they enable faster and more efficient generation of images and other data, with applications in areas like text-to-image synthesis and image editing.